R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8.2,267722,8,266003,7.9,262971,7.6,265521,7.6,264676,8.3,270223,8.4,269508,8.4,268457,8.4,265814,8.4,266680,8.6,263018,8.9,269285,8.8,269829,8.3,270911,7.5,266844,7.2,271244,7.4,269907,8.8,271296,9.3,270157,9.3,271322,8.7,267179,8.2,264101,8.3,265518,8.5,269419,8.6,268714,8.5,272482,8.2,268351,8.1,268175,7.9,270674,8.6,272764,8.7,272599,8.7,270333,8.5,270846,8.4,270491,8.5,269160,8.7,274027,8.7,273784,8.6,276663,8.5,274525,8.3,271344,8,271115,8.2,270798,8.1,273911,8.1,273985,8,271917,7.9,273338,7.9,270601,8,273547,8,275363,7.9,281229,8,277793,7.7,279913,7.2,282500,7.5,280041,7.3,282166,7,290304,7,283519,7,287816,7.2,285226,7.3,287595),dim=c(2,60),dimnames=list(c('wkh','los'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('wkh','los'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
wkh los M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.2 267722 1 0 0 0 0 0 0 0 0 0 0 1
2 8.0 266003 0 1 0 0 0 0 0 0 0 0 0 2
3 7.9 262971 0 0 1 0 0 0 0 0 0 0 0 3
4 7.6 265521 0 0 0 1 0 0 0 0 0 0 0 4
5 7.6 264676 0 0 0 0 1 0 0 0 0 0 0 5
6 8.3 270223 0 0 0 0 0 1 0 0 0 0 0 6
7 8.4 269508 0 0 0 0 0 0 1 0 0 0 0 7
8 8.4 268457 0 0 0 0 0 0 0 1 0 0 0 8
9 8.4 265814 0 0 0 0 0 0 0 0 1 0 0 9
10 8.4 266680 0 0 0 0 0 0 0 0 0 1 0 10
11 8.6 263018 0 0 0 0 0 0 0 0 0 0 1 11
12 8.9 269285 0 0 0 0 0 0 0 0 0 0 0 12
13 8.8 269829 1 0 0 0 0 0 0 0 0 0 0 13
14 8.3 270911 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 266844 0 0 1 0 0 0 0 0 0 0 0 15
16 7.2 271244 0 0 0 1 0 0 0 0 0 0 0 16
17 7.4 269907 0 0 0 0 1 0 0 0 0 0 0 17
18 8.8 271296 0 0 0 0 0 1 0 0 0 0 0 18
19 9.3 270157 0 0 0 0 0 0 1 0 0 0 0 19
20 9.3 271322 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 267179 0 0 0 0 0 0 0 0 1 0 0 21
22 8.2 264101 0 0 0 0 0 0 0 0 0 1 0 22
23 8.3 265518 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 269419 0 0 0 0 0 0 0 0 0 0 0 24
25 8.6 268714 1 0 0 0 0 0 0 0 0 0 0 25
26 8.5 272482 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 268351 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 268175 0 0 0 1 0 0 0 0 0 0 0 28
29 7.9 270674 0 0 0 0 1 0 0 0 0 0 0 29
30 8.6 272764 0 0 0 0 0 1 0 0 0 0 0 30
31 8.7 272599 0 0 0 0 0 0 1 0 0 0 0 31
32 8.7 270333 0 0 0 0 0 0 0 1 0 0 0 32
33 8.5 270846 0 0 0 0 0 0 0 0 1 0 0 33
34 8.4 270491 0 0 0 0 0 0 0 0 0 1 0 34
35 8.5 269160 0 0 0 0 0 0 0 0 0 0 1 35
36 8.7 274027 0 0 0 0 0 0 0 0 0 0 0 36
37 8.7 273784 1 0 0 0 0 0 0 0 0 0 0 37
38 8.6 276663 0 1 0 0 0 0 0 0 0 0 0 38
39 8.5 274525 0 0 1 0 0 0 0 0 0 0 0 39
40 8.3 271344 0 0 0 1 0 0 0 0 0 0 0 40
41 8.0 271115 0 0 0 0 1 0 0 0 0 0 0 41
42 8.2 270798 0 0 0 0 0 1 0 0 0 0 0 42
43 8.1 273911 0 0 0 0 0 0 1 0 0 0 0 43
44 8.1 273985 0 0 0 0 0 0 0 1 0 0 0 44
45 8.0 271917 0 0 0 0 0 0 0 0 1 0 0 45
46 7.9 273338 0 0 0 0 0 0 0 0 0 1 0 46
47 7.9 270601 0 0 0 0 0 0 0 0 0 0 1 47
48 8.0 273547 0 0 0 0 0 0 0 0 0 0 0 48
49 8.0 275363 1 0 0 0 0 0 0 0 0 0 0 49
50 7.9 281229 0 1 0 0 0 0 0 0 0 0 0 50
51 8.0 277793 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 279913 0 0 0 1 0 0 0 0 0 0 0 52
53 7.2 282500 0 0 0 0 1 0 0 0 0 0 0 53
54 7.5 280041 0 0 0 0 0 1 0 0 0 0 0 54
55 7.3 282166 0 0 0 0 0 0 1 0 0 0 0 55
56 7.0 290304 0 0 0 0 0 0 0 1 0 0 0 56
57 7.0 283519 0 0 0 0 0 0 0 0 1 0 0 57
58 7.0 287816 0 0 0 0 0 0 0 0 0 1 0 58
59 7.2 285226 0 0 0 0 0 0 0 0 0 0 1 59
60 7.3 287595 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) los M1 M2 M3 M4
29.0258358 -0.0000766 -0.0102477 -0.0367149 -0.5425832 -0.7034726
M5 M6 M7 M8 M9 M10
-0.8309067 -0.0835689 0.0373316 0.0617585 -0.3584015 -0.4585429
M11 t
-0.4833614 0.0084173
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6682 -0.3207 0.0089 0.2863 0.8284
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.0258358 4.4926249 6.461 5.88e-08 ***
los -0.0000766 0.0000170 -4.505 4.53e-05 ***
M1 -0.0102477 0.2607325 -0.039 0.9688
M2 -0.0367149 0.2618747 -0.140 0.8891
M3 -0.5425832 0.2620410 -2.071 0.0440 *
M4 -0.7034726 0.2602944 -2.703 0.0096 **
M5 -0.8309067 0.2597562 -3.199 0.0025 **
M6 -0.0835689 0.2590790 -0.323 0.7485
M7 0.0373316 0.2589628 0.144 0.8860
M8 0.0617584 0.2596667 0.238 0.8131
M9 -0.3584015 0.2608298 -1.374 0.1761
M10 -0.4585429 0.2600633 -1.763 0.0845 .
M11 -0.4833614 0.2662479 -1.815 0.0760 .
t 0.0084173 0.0060037 1.402 0.1676
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4086 on 46 degrees of freedom
Multiple R-squared: 0.5773, Adjusted R-squared: 0.4578
F-statistic: 4.832 on 13 and 46 DF, p-value: 3.197e-05
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.5359533 9.280935e-01 4.640467e-01
[2,] 0.3689086 7.378171e-01 6.310914e-01
[3,] 0.2928301 5.856601e-01 7.071699e-01
[4,] 0.4954732 9.909465e-01 5.045268e-01
[5,] 0.4566453 9.132907e-01 5.433547e-01
[6,] 0.8746213 2.507573e-01 1.253787e-01
[7,] 0.8889202 2.221597e-01 1.110798e-01
[8,] 0.9183397 1.633206e-01 8.166032e-02
[9,] 0.9023246 1.953508e-01 9.767539e-02
[10,] 0.8974801 2.050397e-01 1.025199e-01
[11,] 0.9799267 4.014658e-02 2.007329e-02
[12,] 0.9994304 1.139295e-03 5.696476e-04
[13,] 0.9999992 1.652251e-06 8.261256e-07
[14,] 0.9999987 2.570256e-06 1.285128e-06
[15,] 0.9999970 5.948593e-06 2.974296e-06
[16,] 0.9999937 1.255389e-05 6.276945e-06
[17,] 0.9999790 4.198092e-05 2.099046e-05
[18,] 0.9999436 1.127684e-04 5.638418e-05
[19,] 0.9998450 3.099829e-04 1.549914e-04
[20,] 0.9995475 9.050233e-04 4.525117e-04
[21,] 0.9990607 1.878639e-03 9.393197e-04
[22,] 0.9978057 4.388511e-03 2.194256e-03
[23,] 0.9948267 1.034657e-02 5.173285e-03
[24,] 0.9903596 1.928078e-02 9.640390e-03
[25,] 0.9720790 5.584201e-02 2.792101e-02
[26,] 0.9473682 1.052636e-01 5.263180e-02
[27,] 0.8970774 2.058452e-01 1.029226e-01
> postscript(file="/var/www/html/rcomp/tmp/1xxlm1258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dp0y1258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/38h1p1258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fexg1258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/535521258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
-0.315405994 -0.629038448 -0.463851024 -0.416038432 -0.361752072 0.007415690
7 8 9 10 11 12
-0.076673943 -0.190028992 0.019249090 0.177312333 0.113189404 0.401488583
13 14 15 16 17 18
0.344991627 -0.054072778 -0.668170585 -0.478640431 -0.262043282 0.488604686
19 20 21 22 23 24
0.772034919 0.828434527 0.322806479 -0.321256797 -0.096307568 -0.089253659
25 26 27 28 29 30
-0.041429120 0.165265053 0.046264585 0.085254435 0.195704863 0.300052296
31 32 33 34 35 36
0.258094910 0.051665895 0.302706476 0.267236129 0.281677327 0.362730784
37 38 39 40 41 42
0.345946412 0.484539551 0.718211029 0.427005598 0.228480075 -0.351558530
43 44 45 46 47 48
-0.342407718 -0.369583169 -0.216257737 -0.115679224 -0.308943373 -0.475046367
49 50 51 52 53 54
-0.334102925 0.033306623 0.367545995 0.382418830 0.199610417 -0.444514142
55 56 57 58 59 60
-0.611048168 -0.320488261 -0.428504308 -0.007612441 0.010384211 -0.199919341
> postscript(file="/var/www/html/rcomp/tmp/6rh3o1258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.315405994 NA
1 -0.629038448 -0.315405994
2 -0.463851024 -0.629038448
3 -0.416038432 -0.463851024
4 -0.361752072 -0.416038432
5 0.007415690 -0.361752072
6 -0.076673943 0.007415690
7 -0.190028992 -0.076673943
8 0.019249090 -0.190028992
9 0.177312333 0.019249090
10 0.113189404 0.177312333
11 0.401488583 0.113189404
12 0.344991627 0.401488583
13 -0.054072778 0.344991627
14 -0.668170585 -0.054072778
15 -0.478640431 -0.668170585
16 -0.262043282 -0.478640431
17 0.488604686 -0.262043282
18 0.772034919 0.488604686
19 0.828434527 0.772034919
20 0.322806479 0.828434527
21 -0.321256797 0.322806479
22 -0.096307568 -0.321256797
23 -0.089253659 -0.096307568
24 -0.041429120 -0.089253659
25 0.165265053 -0.041429120
26 0.046264585 0.165265053
27 0.085254435 0.046264585
28 0.195704863 0.085254435
29 0.300052296 0.195704863
30 0.258094910 0.300052296
31 0.051665895 0.258094910
32 0.302706476 0.051665895
33 0.267236129 0.302706476
34 0.281677327 0.267236129
35 0.362730784 0.281677327
36 0.345946412 0.362730784
37 0.484539551 0.345946412
38 0.718211029 0.484539551
39 0.427005598 0.718211029
40 0.228480075 0.427005598
41 -0.351558530 0.228480075
42 -0.342407718 -0.351558530
43 -0.369583169 -0.342407718
44 -0.216257737 -0.369583169
45 -0.115679224 -0.216257737
46 -0.308943373 -0.115679224
47 -0.475046367 -0.308943373
48 -0.334102925 -0.475046367
49 0.033306623 -0.334102925
50 0.367545995 0.033306623
51 0.382418830 0.367545995
52 0.199610417 0.382418830
53 -0.444514142 0.199610417
54 -0.611048168 -0.444514142
55 -0.320488261 -0.611048168
56 -0.428504308 -0.320488261
57 -0.007612441 -0.428504308
58 0.010384211 -0.007612441
59 -0.199919341 0.010384211
60 NA -0.199919341
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.629038448 -0.315405994
[2,] -0.463851024 -0.629038448
[3,] -0.416038432 -0.463851024
[4,] -0.361752072 -0.416038432
[5,] 0.007415690 -0.361752072
[6,] -0.076673943 0.007415690
[7,] -0.190028992 -0.076673943
[8,] 0.019249090 -0.190028992
[9,] 0.177312333 0.019249090
[10,] 0.113189404 0.177312333
[11,] 0.401488583 0.113189404
[12,] 0.344991627 0.401488583
[13,] -0.054072778 0.344991627
[14,] -0.668170585 -0.054072778
[15,] -0.478640431 -0.668170585
[16,] -0.262043282 -0.478640431
[17,] 0.488604686 -0.262043282
[18,] 0.772034919 0.488604686
[19,] 0.828434527 0.772034919
[20,] 0.322806479 0.828434527
[21,] -0.321256797 0.322806479
[22,] -0.096307568 -0.321256797
[23,] -0.089253659 -0.096307568
[24,] -0.041429120 -0.089253659
[25,] 0.165265053 -0.041429120
[26,] 0.046264585 0.165265053
[27,] 0.085254435 0.046264585
[28,] 0.195704863 0.085254435
[29,] 0.300052296 0.195704863
[30,] 0.258094910 0.300052296
[31,] 0.051665895 0.258094910
[32,] 0.302706476 0.051665895
[33,] 0.267236129 0.302706476
[34,] 0.281677327 0.267236129
[35,] 0.362730784 0.281677327
[36,] 0.345946412 0.362730784
[37,] 0.484539551 0.345946412
[38,] 0.718211029 0.484539551
[39,] 0.427005598 0.718211029
[40,] 0.228480075 0.427005598
[41,] -0.351558530 0.228480075
[42,] -0.342407718 -0.351558530
[43,] -0.369583169 -0.342407718
[44,] -0.216257737 -0.369583169
[45,] -0.115679224 -0.216257737
[46,] -0.308943373 -0.115679224
[47,] -0.475046367 -0.308943373
[48,] -0.334102925 -0.475046367
[49,] 0.033306623 -0.334102925
[50,] 0.367545995 0.033306623
[51,] 0.382418830 0.367545995
[52,] 0.199610417 0.382418830
[53,] -0.444514142 0.199610417
[54,] -0.611048168 -0.444514142
[55,] -0.320488261 -0.611048168
[56,] -0.428504308 -0.320488261
[57,] -0.007612441 -0.428504308
[58,] 0.010384211 -0.007612441
[59,] -0.199919341 0.010384211
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.629038448 -0.315405994
2 -0.463851024 -0.629038448
3 -0.416038432 -0.463851024
4 -0.361752072 -0.416038432
5 0.007415690 -0.361752072
6 -0.076673943 0.007415690
7 -0.190028992 -0.076673943
8 0.019249090 -0.190028992
9 0.177312333 0.019249090
10 0.113189404 0.177312333
11 0.401488583 0.113189404
12 0.344991627 0.401488583
13 -0.054072778 0.344991627
14 -0.668170585 -0.054072778
15 -0.478640431 -0.668170585
16 -0.262043282 -0.478640431
17 0.488604686 -0.262043282
18 0.772034919 0.488604686
19 0.828434527 0.772034919
20 0.322806479 0.828434527
21 -0.321256797 0.322806479
22 -0.096307568 -0.321256797
23 -0.089253659 -0.096307568
24 -0.041429120 -0.089253659
25 0.165265053 -0.041429120
26 0.046264585 0.165265053
27 0.085254435 0.046264585
28 0.195704863 0.085254435
29 0.300052296 0.195704863
30 0.258094910 0.300052296
31 0.051665895 0.258094910
32 0.302706476 0.051665895
33 0.267236129 0.302706476
34 0.281677327 0.267236129
35 0.362730784 0.281677327
36 0.345946412 0.362730784
37 0.484539551 0.345946412
38 0.718211029 0.484539551
39 0.427005598 0.718211029
40 0.228480075 0.427005598
41 -0.351558530 0.228480075
42 -0.342407718 -0.351558530
43 -0.369583169 -0.342407718
44 -0.216257737 -0.369583169
45 -0.115679224 -0.216257737
46 -0.308943373 -0.115679224
47 -0.475046367 -0.308943373
48 -0.334102925 -0.475046367
49 0.033306623 -0.334102925
50 0.367545995 0.033306623
51 0.382418830 0.367545995
52 0.199610417 0.382418830
53 -0.444514142 0.199610417
54 -0.611048168 -0.444514142
55 -0.320488261 -0.611048168
56 -0.428504308 -0.320488261
57 -0.007612441 -0.428504308
58 0.010384211 -0.007612441
59 -0.199919341 0.010384211
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7klf41258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8oq331258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97zkn1258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wp9i1258703708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11wpsy1258703708.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12rruq1258703708.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13gt0d1258703708.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14twq31258703708.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1577p51258703708.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1628yo1258703708.tab")
+ }
>
> system("convert tmp/1xxlm1258703708.ps tmp/1xxlm1258703708.png")
> system("convert tmp/2dp0y1258703708.ps tmp/2dp0y1258703708.png")
> system("convert tmp/38h1p1258703708.ps tmp/38h1p1258703708.png")
> system("convert tmp/4fexg1258703708.ps tmp/4fexg1258703708.png")
> system("convert tmp/535521258703708.ps tmp/535521258703708.png")
> system("convert tmp/6rh3o1258703708.ps tmp/6rh3o1258703708.png")
> system("convert tmp/7klf41258703708.ps tmp/7klf41258703708.png")
> system("convert tmp/8oq331258703708.ps tmp/8oq331258703708.png")
> system("convert tmp/97zkn1258703708.ps tmp/97zkn1258703708.png")
> system("convert tmp/10wp9i1258703708.ps tmp/10wp9i1258703708.png")
>
>
> proc.time()
user system elapsed
2.380 1.548 3.460